本文選題：車(chē)聯(lián)網(wǎng) + 自適應(yīng)控制��； 參考：《大連理工大學(xué)》2015年碩士論文

【摘要】：隨著國(guó)民經(jīng)濟(jì)的快速發(fā)展,城市機(jī)動(dòng)車(chē)的數(shù)量持續(xù)增加。能源消耗與城市交通擁堵、機(jī)動(dòng)車(chē)交通安全等問(wèn)題已成為制約我國(guó)交通運(yùn)輸業(yè)可持續(xù)發(fā)展的瓶頸。城市交通的智能化,信息化,一體化成為了解決城市交通擁堵的最關(guān)鍵的技術(shù)。但是,傳統(tǒng)的交通信號(hào)控制系統(tǒng)主要通過(guò)埋設(shè)靜態(tài)的傳感器或是通過(guò)攝像頭紅外線等手段進(jìn)行交通流的統(tǒng)計(jì)及預(yù)測(cè)。這不僅僅造成了資源的嚴(yán)重浪費(fèi),同時(shí)對(duì)交通流難以進(jìn)行精確的衡量,只能是利用歷史數(shù)據(jù)或是依據(jù)傳感器采集到的單一數(shù)據(jù),進(jìn)行交通流的調(diào)控。車(chē)聯(lián)網(wǎng)的出現(xiàn)為徹底的解決城市交通擁堵帶來(lái)了希望。車(chē)聯(lián)網(wǎng)是將車(chē)車(chē)、車(chē)路之間的通信加入到了城市交通控制系統(tǒng)中,使得行駛的車(chē)輛可以與周?chē)能?chē)輛、路邊基礎(chǔ)設(shè)施以及廣域網(wǎng)進(jìn)行無(wú)線通信和信息交換,建立高效的交叉口行車(chē)控制、車(chē)輛安全駕駛和實(shí)時(shí)信息服務(wù)等的一體化網(wǎng)絡(luò)。但是,由于車(chē)聯(lián)網(wǎng)的發(fā)展處于初步階段,對(duì)于大規(guī)模的城市交通流的控制疏散的研究較少。因此,本文提出了車(chē)聯(lián)網(wǎng)環(huán)境下基于路徑優(yōu)先級(jí)的自適應(yīng)控制。該方法首先設(shè)計(jì)車(chē)聯(lián)網(wǎng)環(huán)境中車(chē)路通信的系統(tǒng)框架及結(jié)構(gòu),然后對(duì)整個(gè)城市路網(wǎng)進(jìn)行抽象建模,將城市路網(wǎng)抽象為復(fù)雜的有向圖。接著利用車(chē)聯(lián)網(wǎng)中的車(chē)路通信方式,通過(guò)路側(cè)單元收集統(tǒng)計(jì)交通流的實(shí)時(shí)狀態(tài)信息。路側(cè)單元通過(guò)PageRank模型定量的表示出每股交通流的放行優(yōu)先級(jí)。最后,在單個(gè)交叉口處通過(guò)加權(quán)最大團(tuán)模型,計(jì)算出交通流放行的最優(yōu)相位、相序。使得單次放行的交通流,即保證了高優(yōu)先級(jí)的交通流優(yōu)先通過(guò),也保證了放行的車(chē)流量最大。最后,本文的實(shí)驗(yàn)分為兩種方式進(jìn)行。一種方式是在VISSIM仿真平臺(tái)上進(jìn)行,主要測(cè)試本文提出的自適應(yīng)控制策略對(duì)于車(chē)輛調(diào)度的效果。另一種方式是真實(shí)車(chē)輛的現(xiàn)場(chǎng)測(cè)試,主要測(cè)試系統(tǒng)的功能。結(jié)果表明,本文提出的基于路徑優(yōu)先級(jí)的自適應(yīng)控制方法,相比較于傳統(tǒng)的自適應(yīng)控制方法控制效果較好。同時(shí),在現(xiàn)實(shí)的真車(chē)環(huán)境下能夠高效的完成交叉口處車(chē)輛的調(diào)度。
[Abstract]:With the rapid development of the national economy, the number of urban motor vehicles continues to increase. Energy consumption, urban traffic congestion, motor vehicle traffic safety and other problems have become the bottleneck of sustainable development of transportation industry in China. The intelligence, information and integration of urban traffic have become the most critical technology to solve urban traffic congestion. However, the traditional traffic signal control system mainly uses static sensors or infrared camera to calculate and predict traffic flow. This is not only a serious waste of resources, but also difficult to accurately measure traffic flow. It can only use historical data or single data collected by sensors to regulate traffic flow. The emergence of car networking has brought hope for a thorough solution to urban traffic congestion. Vehicle networking is the addition of communication between vehicles and roads to the urban traffic control system so that vehicles can communicate and exchange information wirelessly with surrounding vehicles, roadside infrastructure and wide area networks. An integrated network of efficient intersection traffic control, vehicle safe driving and real-time information service is established. However, due to the initial stage of the development of vehicle networking, there is little research on large-scale urban traffic flow control evacuation. Therefore, an adaptive control based on path priority is proposed in this paper. This method first designs the system framework and structure of vehicle-road communication in the vehicle networking environment, and then abstracts the whole urban road network into a complex directed graph. Then, the real-time state information of traffic flow is collected through road-side unit by means of vehicle-road communication in vehicle networking. The kerbside unit quantificationally represents the priority of traffic flow per share through the PageRank model. Finally, the optimal phase and phase sequence of the traffic flow are calculated by using the weighted maximum cluster model at a single intersection. The single release traffic flow ensures the priority of high priority traffic flow and the maximum traffic flow. Finally, the experiment is divided into two ways. One method is carried out on the VISSIM simulation platform, which mainly tests the effect of the adaptive control strategy proposed in this paper on vehicle scheduling. Another way is the real vehicle field test, the main test system function. The results show that the proposed adaptive control method based on path priority is better than the traditional adaptive control method. At the same time, the vehicle scheduling at the intersection can be completed efficiently in the real vehicle environment.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491.54;TN929.5;TP391.44

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本文編號(hào)：1787679